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October 11, 2018

After we coupled the Tile Processor (TP) that performs quad camera image conditioning and produces 2D phase correlation in space-invariant form with the neural network[1], the TP remained the bottleneck of the tandem. While the inferred network uses GPU and produces disparity output in 0.5 sec (more than 80% of this time is used for the data transfer), the TP required tens of seconds to run on CPU as a multithreaded Java application. When converted to run on the GPU, similar operation takes just 0.087 seconds for four 5 MPix images, and it is likely possible to optimize the code farther — this is our first experience with Nvidia® CUDA™.

September 5, 2018

Neural network connected to the output of the Tile Processor (TP) reduced the disparity error twice from the previously used heuristic algorithms. The TP corrects optical aberrations of the high resolution stereo images, rectifies images, and provides 2D correlation outputs that are space-invariant and so can be efficiently processed with the neural network.

What is unique in this project compared to other ML applications for image-based 3D reconstruction is that we deal with extremely long ranges (and still wide field of view), the disparity error reduction means 0.075 pix standard deviation down from 0.15 pix for 5 MPix images.

July 21, 2018

In this blog article we will recall the most interesting results of Elphel participation at CVPR 2018 Expo, the conversations we had with visitor’s at the booth, FAQs as well as unusual questions, and what we learned from it. In addition we will explain our current state of development as well as our near and far goals, and how the exhibition helps to achieve them.
The Expo lasted from June 19-21, and each day had it’s own focus and results, so this article is organized chronologically.

Day One: The best show ever!

June 19, CVPR 2018, booth 132

While we are standing nervously at our booth, thinking: “Is there going to be any interest? Will people come, will they ask questions?”, the first poster session starts and a wave of visitors floods the exhibition floor. Our first guest at the booth spends 30 minutes, knowledgeably inquiring about Elphel’s long-range 3D technology and leaves his business card, saying that he is very impressed. This was a good start of a very busy day full of technical discussions. CVPR is the first exhibition we have participated in where we did not have any problems explaining our projects.

The most common questions that were asked:

July 20, 2018

We uploaded an image set with 2D correlation data together with the import Python code for experiments with the neural networks and are now looking for collaboration with those who would love to apply their DL experience to the new kind of input data. More data will follow and we welcome feedback to make this data set more useful.

The application area we are interested in is an extremely long distance 3D scene reconstruction and ranging with the distance to baseline ratio of 1000:1 to 10,000:1 while preserving wide field of view. Earlier post describes aircraft distance and velocity measurements with up to 3,000:1 distance-to-baseline ratio and 60°(H)×45°(V) field of view.

Figure 1. Space-invariant 2D phase correlation data

Data set: source images, 2D correlation tiles, and X3D scene models

The data set contains 2D phase correlation output calculated from the 2592×1936 Bayer mosaic source images captured by the quad stereo camera, and Disparity Space Image (DSI) calculated from a pair of such cameras. Longer baseline provides higher range resolution and this DSI is serving as the ground truth for a single quad camera. The source images as well as all the used software is also provided under the GNU/GPLv3 license. DSI is organized as a 324×242 array – each sample is calculated from the corresponding 16×16 tile. Tiles are overlapping (as shown in Figure 3) with stride 8.

These data sets are provided together with the fully reconstructed X3D scene models, viewable in the browser (Wavefront OBJ files are also generated). The scene models are different from the raw DSI as the next software stages generate meshes, and that frequently leads to over-simplification of the original DSI (so most fronto parallel objects in the scene provide better range accuracy when probed near the bottom). Each scene is accompanied with the multi-layer TIFF file (*-DSI_COMBO.tiff) that allows to see the difference between the measured DSI and the one used in the rendered X3D model. File format and Python import software is documented in Oleg’s post “Reading quad stereo TIFF image stacks in Python and formatting data for TensorFlow”, the data files are directly viewable with ImageJ.

The input is a <filename>.tiff – a TIFF image stack generated by ImageJ Java plugin (using bioformats) with Elphel-specific information in ImageJ written TIFF tags.
Reading and formatting image data for the Tensorflow can be split into the following subtasks:

convert a TIFF image stack into a NumPy array

extract information from the TIFF header tags

reshape/perform a few array manipulations based on the information from the tags.

To do this we have created a few Python scripts (see python3-imagej-tiff: imagej_tiff.py) that use Pillow, Numpy, Matplotlib, etc..
Usage:
~$ python3 imagej_tiff.py <filename>.tiff
It will print header info in the terminal and display the layers (and decoded values) using Matplotlib.
(more…)

May 6, 2018

Figure 1. Aircraft positions during descent captured with the quad stereo camera. Each animation frame corresponds to the available 3-D model.

While we continue to work on the multi-sensor stereo camera hardware (we plan to double the number of sensors to capture single-exposure HDR image sets) and develop code to get the ground truth data for the CNN training, we had some fun testing the camera for capturing aircraft position in 3-D space. Completely passive method, of course.

We found a suitable spot about 2.5 km from the beginning of the runway 34L of the Salt Lake City international airport (exact location is shown in the model viewer) so approaching aircraft would pass almost over our heads. With the 60°(H)×45°(V) field of view of the camera aircraft are visible when they are 270 m away horizontally.

April 20, 2018

MT9F002

This post briefly covers implementation of a driver for On Semi’s MT9F002 14MPx image sensor for 10393 system boards – the steps are more or less general. The driver is included in the latest software/firmware image (20180416). The implemented features are programmable:

window size

horizontal & vertical mirror

color gains

exposure

fps and trigger-synced ports

frame-based commands sequence allowing to change settings of any image up to 16 frames ahead (didn’t need to be implemented as it’s the common part of the driver for all sensors)

March 20, 2018

Following the plan laid out in the earlier post we’ve built a camera rig for capturing training/testing image sets. The rig consists of the two quad cameras as shown in Figure 1. Four identical Sensor Front Ends (SFE) 10338E of each camera use 5 MPix MT9P006 image sensors, we will upgrade the cameras to 18 MPix SFE later this year, the circuit boards 103981 are in production now.

February 5, 2018

This article describes our next steps that will continue the year-long research on high resolution multi-view stereo for long distance ranging and 3-D reconstruction. We plan to fuse the methods of high resolution images calibration and processing, already emulated functionality of the Tile Processor (TP), RTL code developed for its implementation and the Convolutional Neural Network (CNN). Compared to the CNN alone this approach promises over a hundred times reduction in the number of input features without sacrificing universality of the end-to-end processing. The TP part of the system is responsible for the high resolution aspects of the image acquisition (such as optical aberrations correction and image rectification), preserves deep sub-pixel super-resolution using efficient implementation of the 2-D linear transforms. Tile processor is free of any training, only a few hyperparameters define its operation, all the application-specific processing and “decision making” is delegated to the CNN.